Simpler stacktraces for the Julia Programming Language

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

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A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages

Basically just a surrogate in disguise

Surrogate modeling and optimization for scientific machine learning (SciML)

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

Fast and automatic structural identifiability software for ODE systems

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Stochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem

Solvers for steady states in scientific machine learning (SciML)

Static types useful for dispatch and generated functions.

Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

Simple differential equation solvers in native Julia for scientific machine learning (SciML)

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Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization

Workshop materials for training in scientific computing and scientific machine learning

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

A structure interface for SciML to give queryable properties from user data and parameters

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia

Nonlinear solver bindings for the SciML Interface

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

The Base interface of the SciML ecosystem

Functions to run the SBML Test Suite with SBMLToolkit, create logs and create reports for the SBML Test Suite Database

SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit

Functions generated at runtime without world-age issues or overhead

A collection of functionality around rooted trees to generate order conditions for Runge-Kutta methods in Julia for differential equations and scientific machine learning (SciML)

A stack implementation with a reset! function which avoids garbage collection for scientific machine learning (SciML)

Reservoir computing utilities for scientific machine learning (SciML)

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices

Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)

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A wrapper for the Python PyDSTool library for the SciML Scientific Machine Learning organization

Generation of reaction networks from PubChem data

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GPU accelerated Particle Swarm Optimization